Investigating Sources of Randomness and Uncertainty in a Stiff Biological System (B)

using DifferentialEquations
using Sundials
using BenchmarkTools
using Plots

Part 1: Simulating the Oregonator ODE model

using DifferentialEquations, Plots
function orego(du,u,p,t)
  s,q,w = p
  y1,y2,y3 = u
  du[1] = s*(y2+y1*(1-q*y1-y2))
  du[2] = (y3-(1+y1)*y2)/s
  du[3] = w*(y1-y3)
end
p = [77.27,8.375e-6,0.161]
prob = ODEProblem(orego,[1.0,2.0,3.0],(0.0,360.0),p)
sol = solve(prob)
plot(sol)

plot(sol,idxs=(1,2,3))

Part 2: Investigating Stiffness

using BenchmarkTools
prob = ODEProblem(orego,[1.0,2.0,3.0],(0.0,50.0),p)
@btime sol = solve(prob,Tsit5());

  883.426 ms (7850826 allocations: 655.45 MiB)

@btime sol = solve(prob,Rodas5());

  307.996 μs (888 allocations: 75.39 KiB)

(Optional) Part 3: Specifying Analytical Jacobians (I)

(Optional) Part 4: Automatic Symbolicification and Analytical Jacobian Calculations

Part 5: Adding stochasticity with stochastic differential equations

function orego(du,u,p,t)
  s,q,w = p
  y1,y2,y3 = u
  du[1] = s*(y2+y1*(1-q*y1-y2))
  du[2] = (y3-(1+y1)*y2)/s
  du[3] = w*(y1-y3)
end
function g(du,u,p,t)
  du[1] = 0.1u[1]
  du[2] = 0.1u[2]
  du[3] = 0.1u[3]
end
p = [77.27,8.375e-6,0.161]
prob = SDEProblem(orego,g,[1.0,2.0,3.0],(0.0,30.0),p)
sol = solve(prob,SOSRI())
plot(sol)

sol = solve(prob,ImplicitRKMil()); plot(sol)

sol = solve(prob,ImplicitRKMil()); plot(sol)

Part 6: Gillespie jump models of discrete stochasticity

Part 7: Probabilistic Programming / Bayesian Parameter Estimation with DiffEqBayes.jl + Turing.jl (I)

The data was generated with:

function orego(du,u,p,t)
  s,q,w = p
  y1,y2,y3 = u
  du[1] = s*(y2+y1*(1-q*y1-y2))
  du[2] = (y3-(1+y1)*y2)/s
  du[3] = w*(y1-y3)
end
p = [60.0,1e-5,0.2]
prob = ODEProblem(orego,[1.0,2.0,3.0],(0.0,30.0),p)
sol = solve(prob,Rodas5(),abstol=1/10^14,reltol=1/10^14)
plot(sol)